1. Field of the Invention
The present invention relates to the field of computer question-answering, and in particular to an improved computer learning method and system for computer question-answering, and a method and system for responding to a new question.
2. Description of the Related Art
A question-answering system, also referred to as Human Machine Conversation (HMC) system, refers to a system that receives from a user a question expressed in the form of a natural language, and obtains, from a large amount of structural, semi-structural or non-structural data, an accurate, concise and individualized answer to the question in the form of the natural language.
Question answer systems are playing an increasingly important role in various fields, such as assisting in diagnosis, self-medicating, in the health care/life science field, used in call centers, self-services, in the retailing/consumption field, assisting enterprises' decisions in the field of enterprise business intelligence, and many others.
However, in the prior art, when a correct answer is not included in the candidate answer set, since the data is not related to the machine learning, no matter how the model is trained, a correct answer to the question cannot be found, and the machine learning cannot achieve a good resolution. Thus, it can be seen that there is still room for improvement in the question-answering method and systems known in the prior art.